R Notebook

library(tidyverse)

── Attaching core tidyverse packages ────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     ── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(ratdat)

#exploration de données 
?complete_old
summary(complete_old)

   record_id         month             day            year         plot_id       species_id            sex           
 Min.   :    1   Min.   : 1.000   Min.   : 1.0   Min.   :1977   Min.   : 1.00   Length:16878       Length:16878      
 1st Qu.: 4220   1st Qu.: 3.000   1st Qu.: 9.0   1st Qu.:1981   1st Qu.: 5.00   Class :character   Class :character  
 Median : 8440   Median : 6.000   Median :15.0   Median :1983   Median :11.00   Mode  :character   Mode  :character  
 Mean   : 8440   Mean   : 6.382   Mean   :15.6   Mean   :1984   Mean   :11.47                                        
 3rd Qu.:12659   3rd Qu.: 9.000   3rd Qu.:23.0   3rd Qu.:1987   3rd Qu.:17.00                                        
 Max.   :16878   Max.   :12.000   Max.   :31.0   Max.   :1989   Max.   :24.00                                        
                                                                                                                     
 hindfoot_length     weight          genus             species              taxa            plot_type        
 Min.   : 6.00   Min.   :  4.00   Length:16878       Length:16878       Length:16878       Length:16878      
 1st Qu.:21.00   1st Qu.: 24.00   Class :character   Class :character   Class :character   Class :character  
 Median :35.00   Median : 42.00   Mode  :character   Mode  :character   Mode  :character   Mode  :character  
 Mean   :31.98   Mean   : 53.22                                                                              
 3rd Qu.:37.00   3rd Qu.: 53.00                                                                              
 Max.   :70.00   Max.   :278.00                                                                              
 NA's   :2733    NA's   :1692                                                                                

head(complete_old)

str(complete_old)

tibble [16,878 × 13] (S3: tbl_df/tbl/data.frame)
 $ record_id      : int [1:16878] 1 2 3 4 5 6 7 8 9 10 ...
 $ month          : int [1:16878] 7 7 7 7 7 7 7 7 7 7 ...
 $ day            : int [1:16878] 16 16 16 16 16 16 16 16 16 16 ...
 $ year           : int [1:16878] 1977 1977 1977 1977 1977 1977 1977 1977 1977 1977 ...
 $ plot_id        : int [1:16878] 2 3 2 7 3 1 2 1 1 6 ...
 $ species_id     : chr [1:16878] "NL" "NL" "DM" "DM" ...
 $ sex            : chr [1:16878] "M" "M" "F" "M" ...
 $ hindfoot_length: int [1:16878] 32 33 37 36 35 14 NA 37 34 20 ...
 $ weight         : int [1:16878] NA NA NA NA NA NA NA NA NA NA ...
 $ genus          : chr [1:16878] "Neotoma" "Neotoma" "Dipodomys" "Dipodomys" ...
 $ species        : chr [1:16878] "albigula" "albigula" "merriami" "merriami" ...
 $ taxa           : chr [1:16878] "Rodent" "Rodent" "Rodent" "Rodent" ...
 $ plot_type      : chr [1:16878] "Control" "Long-term Krat Exclosure" "Control" "Rodent Exclosure" ...

#ggplot

#ggplot
library(ggplot2)
ggplot(data=complete_old, mapping=aes(x = weight, y= hindfoot_length, color=plot_type))+
  geom_point(alpha=0.2)

#enlever valeurs manquantes
complete_old <- filter(complete_old, !is.na(weight))
complete_old <- filter(complete_old, !is.na(hindfoot_length))

#library(ggplot2)
ggplot(data=complete_old, mapping=aes(x = weight, y= hindfoot_length, color=plot_type, shape=sex))+
  geom_point(alpha=0.2) +
  scale_color_viridis_d() +
  scale_x_log10()

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_boxplot() +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_boxplot() +
  geom_jitter(alpha = 0.1) +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length, color=plot_type ))+
  geom_boxplot() +
  geom_jitter(alpha = 0.1) +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_boxplot() +
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_boxplot(outlier.shape=NA) +
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_boxplot(outlier.shape=NA) +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_boxplot(outlier.shape=NA, fill=NA) +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length, fill=plot_type ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_violin() +
  scale_x_discrete(labels = label_wrap_gen(width=10))

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_boxplot(outlier.shape=NA, fill=NA) +
  scale_x_discrete(labels = label_wrap_gen(width=10))+
  theme_bw()

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_boxplot(outlier.shape=NA, fill=NA) +
  scale_x_discrete(labels = label_wrap_gen(width=10))+
  theme_bw()+
  theme(legend.position = "none") +
  labs(x="Plot type", y = "Hindfoot length (mm)")

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_boxplot(outlier.shape=NA, fill=NA) +
  scale_x_discrete(labels = label_wrap_gen(width=10))+
  theme_bw()+
  theme(lengend.position = "none") +
  labs(x="Plot type", y = "Hindfoot length (mm)")+
  facet_wrap(vars(sex), nrow = 1)

ggplot(data=complete_old, mapping=aes(x = plot_type, y= hindfoot_length ))+
  geom_jitter(alpha = 0.1, aes(color=plot_type)) +
  geom_boxplot(outlier.shape=NA, fill=NA) +
  scale_x_discrete(labels = label_wrap_gen(width=10))+
  theme_bw()+
  theme(lengend.position = "none") +
  labs(x="Plot type", y = "Hindfoot length (mm)")+
  facet_wrap(vars(sex), ncol = 1)

ggsave(filename = "Figures/plot_final.png", plot = plot_final, height = 6, width = 8)

Warning: The `lengend.position` theme element is not defined in the element hierarchy.

#tidyverse

surveys <- read_csv("../Data/raw/surveys_complete_77_89.csv")

Rows: 16878 Columns: 13── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (6): species_id, sex, genus, species, taxa, plot_type
dbl (7): record_id, month, day, year, plot_id, hindfoot_length, weight
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#select()
#filter()
#mutate()
#group_by()
#summarize() 

##select

select(surveys, plot_id,species_id)

select(surveys, c(3,4))

select(surveys, -plot_id)

select(surveys, where(is.numeric))

select(surveys, where(anyNA))

##filter

filter(surveys, year == 1988)

filter(surveys, species_id %in% c("RM","DO"))

filter(surveys, year == 1988 & species_id %in% c("RM","DO"))

#chalenge
# 1er facon
surveys_80_85 <- filter(surveys, year >= 1980 & year <=1985)
surveys_80_85 <- select(surveys_80_85, year, month, plot_id, species_id)
surveys_80_85

select(filter(surveys, year >= 1980 & year <=1985), year, month, plot_id, species_id )

##pipelines %>%

#3eme facon
surveys %>% filter(year==1980:1985) %>% select(year, month, plot_id, species_id)

#chalenge 2 
surveys %>% filter(year==1988) %>% select(record_id, month, species_id)

##mutate

surveys %>% mutate(weight_kg = weight / 100) %>% relocate(weight_kg, .after = record_id)

surveys %>% mutate(weight_kg = weight / 100, weight_lbs = weight_kg*2.2) %>% relocate(weight_lbs, .after = record_id)

surveys %>% mutate(weight_kg = weight / 100, weight_lbs = weight_kg*2.2) %>% relocate(weight_lbs, .after = record_id) %>% relocate(weight_kg, .after = record_id)

surveys %>% filter(!is.na(weight)) %>% mutate(weight_kg = weight / 100, weight_lbs = weight_kg*2.2) %>% relocate(weight_lbs, .after = record_id) %>% relocate(weight_kg, .after = record_id)

surveys %>% mutate(date = paste(year, month, day, sep = "-")) %>% relocate(date, .after = year)

library(lubridate)

surveys %>% mutate(date = ymd(paste(year, month, day, sep = "-"))) %>% relocate(date, .after = year)

##group_by & summarize

surveys %>% group_by(sex) %>% summarize(mean.weight = mean(weight))

surveys %>% group_by(sex) %>% summarize(mean.weight = mean(weight, na.rm = T), count = n())

#chalenge 3 
surveys %>% mutate(date = ymd(paste(year, month, day, sep = "-"))) %>% 
  filter(!is.na(sex)) %>%
  group_by(sex,date) %>% 
  summarize(count = n()) %>% 
  ggplot(aes(x=date, y= count, color=sex)) +
  geom_line() +
  theme_bw()

`summarise()` has grouped output by 'sex'. You can override using the `.groups` argument.

NA
NA